My research interest is on developing algorithms for analysing biological data, mostly gene expression data from large-scale EST sequencing and microarray experiments, and integrating information from different sources to find biological interesting and important genes, and how they are regulated. These algorithms have been appliedon expression datawith the aimto identify important cluesto the frost tolerance mechanisms in oat and related crop species.
As a bioinformaticist you have to be comfortable in one or more research fields, such as computer science, statistics or mathematics, and also have a good foundation in biology. In my casethe research fieldis computer science, when I am developing algorithms for analysing biological data, and molecular biology, when I am trying to find clues to frost mechanisms in oat. I find these two fields equally important and interesting, and could never choose one over the other. They complement each other in my research; without the computer science I could never analyse the huge amount of data coming from biological experiments, and without the biology I could never understand the results I get from the analyses and know how to proceed with further analyses.
However, I am more of a computer scientist than a molecular biologist, since I prefer to sit in front of the computer than spending timedoingwet lab. Recently I have also taken an interest in statistics and is currently trying to learn more about this area and how I can apply it to biological data.
- Large-scale EST data
In 2002 we sequenced ~10 000 ESTs from cold acclimated oat, which was subsequently analysed with the result that a number of cold related genes were identified. This data was thereafter used together with EST data from other cold acclimated crop species as well as Arabidopsis thalianain a comparison study. This study revealed a great diversity among the genes detected as differentially expressed.
I also wrote a paper
that aimed at evaluating if 'digital-Northern' could be applied to small-scaled EST sets and if the combination of several statistical methods would improve the results from 'digital-Northern'.
- Microarray data
Currently, a rice cold acclimation experiment is compared to a Arabidopsis thaliana cold acclimation experiment. Later on, cold acclimation experiments for a number of oat varieties will be conducted and analysed, for identifying differences among oat varieties with different degrees of frost tolerance.
- Hormon related breast cancer
Recently we have started a collaboration with researchers at Örebro University Hospital. This collaboration is focused on finding treatment markers for those have developed a hormon (aromatase-eostrogen) related breast cancer.
- Combinatorial control of transcription
Many biological processes, such as cold acclimation, is under combinatorial control on the transcriptional level, i.e., more than one transcription factor work in synergism to regulate the expression of gene(s). The issue here is to identify which transcription factors work in concert and which genes they regulate.
Program and aids
Programs and aids that I use in my research.
- Programming languages: PHP and Perl
- Phylogeny: Phylip, ClustalW and FigTree
- EST analysis: EGassembler, Cap3, R statistics and IDEG6
- Microarray analysis: R statistics and HCE'
- Orthology/sequence similarities: OrthoMCL and GreenPhyl
- Statistical analysis: R statistics and Excel
- Data mining: R statistics and Weka
- Visualization: Treedyn, Treeview, GraphViz and R statistics
- Databases and other information sources: TAIR,TIGR, Gene Ontology, MapMan